Evaluating the Ripeness of Produce with Near-Infrared Spectrometers
Even when produce looks great, its quality of taste and shelf life can be quite different. However, scientists at the Fraunhofer Institute for Photonic Microsystems (IPMS) in Dresden are now able to tell how ripe a pear or tomato is using a near-infrared spectrometer.
We’ve all been there before– You go to store, buy delicious looking produce, and one day later, it’s already moulding. This may be because of the way it was stored. Or maybe the fruit had become bruised after being bagged under other groceries. Perhaps the produce had already surpassed its perfect degree of ripeness. Either way, a lot of fruit goes to waste when it becomes bruised– even before it reaches the end consumer. In fact, up to 56 percent of all fresh produce never reaches the customer because it spoils while in storage, transportation or while sitting on the store shelf.
However, by recognizing the produce’s degree of ripeness in time, more food can be used and less can be wasted. For example, ripe, good-looking pears could be sold to the consumer. Old pears with bruises could be used for fruit juice, and moulding pears for biofuel. In short, using produce according to its degree of ripeness ultimately leads to more sustainable economic growth.
Infrared Technology Capable of Detecting Damaged Product
Fraunhofer researchers at IPMS succeeded in detecting where produce is damaged– even before it begins to spoil. To do so, they use hyperspectral imaging in the near infrared (NIR) region of the visual spectrum. Hyperspectral imaging is a combination of spectroscopy and digital image capture. This technology can even identify the food sort by using spectral analysis and simultaneous object recognition. In comparison to the naked eye, this technology has a clear advantage as it can look inside the fruit and gather information from within.
How Hyperspectral Imaging Systems Work
Pressure causes the molecule structure of fruit to change. In reaction to this, the affected area starts moulding after a few days. However, with NIR Hyperspectral Imaging, moulding can be detected even before it becomes visible. Hyperspectral Imaging systems typically work like a line scan camera does. It captures several sliced images of an object. The 2D image of the object is then scanned by an encoder in sequence. This all happens while the object is being put in motion by, for example, a conveyer belt. To capture the image, a one-dimensional InGaAs detector array is used.
Hyperspectral imaging systems can be installed directly into sorting equipment. As soon as a database has been entered into the system, it can start sorting produce according to its degree of ripeness and quality. However, before this device can be brought onto the market, researchers still need to develop a much larger database.